Many computer applications provide a graphical user interface for input and output actions which provide a graphical representation of a human body. In this regard virtual object models of human bodies have to be created, which provide for a human body representation in a computer-generated, virtual space, which may be visualized on a computer monitor for different purposes, like for example robotic applications, medical applications and game applications and many others. The models of such “virtual humans” are commonly referred to as “avatars”.
Some applications need to align or match the general representations to real physically measured 3D scans, which may be acquired by laser scanners in order to generate a model for the object or person respectively.
Building a model of the body typically has three stages:                1) initialization,        2) registration and        3) model building.        
Registering a corpus of (human) body scans involves bringing them into alignment with a common human-shaped template.
To provide plausible alignments of ambiguous meshes, existing alignment algorithms tend to employ simple priors motivated by analogy to deformations of physical objects—the template should deform elastically like rubber, or smoothly like taffy. When registering scans with a common template mesh, such priors yield geometric regularization terms that prevent the template from undergoing wildly implausible deformations. Unfortunately, it is difficult to get adequate constraints from these priors while retaining the flexibility to fit a wide range of poses and shapes.
Strong 3D shape priors enable robust and accurate inference. Building strong shape priors from data, however, is difficult, even with dense accurate measurements from high-end 3D scanners. Methods for learning shape models depend on identifying corresponding points across many 3D scans; that is, registered data. Establishing such correspondences is particularly difficult for articulated shapes such as people with highly varied shapes and poses. Across such scans one can usually identify some key anatomical landmarks (e.g. the tip of the nose, the corners of the eyes) but there are large regions of the body where it is difficult to find useful correspondences, automatically or manually.